def __init__(self): self.vision = Images() self.knowledgeBase = []
def __init__(self):
self.vision = Images()
self.knowledgeBase = []
class Savant():
def __init__(self):
self.vision = Images()
self.knowledgeBase = []
def loadKB(self):
self.knowledgeBase = [x[14:] for x in glob.glob("knowledgeBase/*")]
print "current known commands:"
for knowledge in self.knowledgeBase:
print knowledge.replace("_"," ") #["train vision"] #need to load these from a file
def learnAction(self,action):
if raw_input("I have never done this before, should I attempt to learn it now? (y/n): ") == "y":
actionSpecs = open("knowledgeBase/"+action.replace(" ","_"),"w")
inputLayerUnits = input("How many input units do I need? ")
hiddenLayerUnits = input("How many hidden units do I need? ")
outputLayerUnits = input("How many output units do I need? ")
actionSpecs.write(str(inputLayerUnits))
actionSpecs.write("\n")
actionSpecs.write(str(hiddenLayerUnits))
actionSpecs.write("\n")
actionSpecs.write(str(outputLayerUnits))
actionSpecs.close()
net = NeuralNetwork(action)
print "making neural network..."
net.makeNetwork(inputLayerUnits,hiddenLayerUnits,outputLayerUnits)
print "initializing random weights..."
net.initializeRandomWeights()
print "generating input activations..."
net.genInputActivations()
try:
print "training..."
net.train()
except KeyboardInterrupt:
net.saveWeights()
print "weights saved"
return
except RuntimeError:
print "Training failed."
net.saveWeights()
print "weights saved"
return
print "Success!"
net.saveWeights()
print "weights saved"
self.knowledgeBase.append(action)
print "testing..."
net.test()
return
else:
print "Ok, maybe later."
return
def performAction(self,action):
actionSpecs = open("knowledgeBase/"+action.replace(" ","_"),"r")
inputLayerUnits = actionSpecs.readline()
hiddenLayerUnits = actionSpecs.readline()
outputLayerUnits = actionSpecs.readline()
actionSpecs.close()
net = NeuralNetwork(action)
print "making neural network..."
net.makeNetwork(int(inputLayerUnits),int(hiddenLayerUnits),int(outputLayerUnits))
print "generating input activations..."
net.genInputActivations()
print "loading weights..."
net.loadWeights("weights/"+action.replace(" ","_"))
if raw_input("Continue training? (y/n): ") == "y":
try:
print "training..."
net.train()
except KeyboardInterrupt:
net.saveWeights()
print "weights saved"
except RuntimeError:
print "Training failed."
net.saveWeights()
print "weights saved"
#need to do minus/plus phase here
def trainVision(self):
net = NeuralNetwork("vision")
net.makeNetwork(1024,25,2)
if raw_input("Continue Previous Training? (y/n): ") == "y":
print "loading weights..."
net.loadWeights("weights/vision_2")
else:
print "initializing random weights..."
net.initializeRandomWeights()
print "generating input activations..."
net.genInputActivations()
try:
print "training..."
net.train()
except KeyboardInterrupt:
net.saveWeights()
print "weights saved"
def loadVision(self):
#index = input("Enter image index: ")
for index in range(len(self.vision.images["data"])-1):
self.vision.saveImage(index)
class Savant():
def __init__(self):
self.vision = Images()
self.knowledgeBase = []
def loadKB(self):
self.knowledgeBase = [x[14:] for x in glob.glob("knowledgeBase/*")]
print "current known commands:"
for knowledge in self.knowledgeBase:
print knowledge.replace(
"_", " ") #["train vision"] #need to load these from a file
def learnAction(self, action):
if raw_input(
"I have never done this before, should I attempt to learn it now? (y/n): "
) == "y":
actionSpecs = open("knowledgeBase/" + action.replace(" ", "_"),
"w")
inputLayerUnits = input("How many input units do I need? ")
hiddenLayerUnits = input("How many hidden units do I need? ")
outputLayerUnits = input("How many output units do I need? ")
actionSpecs.write(str(inputLayerUnits))
actionSpecs.write("\n")
actionSpecs.write(str(hiddenLayerUnits))
actionSpecs.write("\n")
actionSpecs.write(str(outputLayerUnits))
actionSpecs.close()
net = NeuralNetwork(action)
print "making neural network..."
net.makeNetwork(inputLayerUnits, hiddenLayerUnits,
outputLayerUnits)
print "initializing random weights..."
net.initializeRandomWeights()
print "generating input activations..."
net.genInputActivations()
try:
print "training..."
net.train()
except KeyboardInterrupt:
net.saveWeights()
print "weights saved"
return
except RuntimeError:
print "Training failed."
net.saveWeights()
print "weights saved"
return
print "Success!"
net.saveWeights()
print "weights saved"
self.knowledgeBase.append(action)
print "testing..."
net.test()
return
else:
print "Ok, maybe later."
return
def performAction(self, action):
actionSpecs = open("knowledgeBase/" + action.replace(" ", "_"), "r")
inputLayerUnits = actionSpecs.readline()
hiddenLayerUnits = actionSpecs.readline()
outputLayerUnits = actionSpecs.readline()
actionSpecs.close()
net = NeuralNetwork(action)
print "making neural network..."
net.makeNetwork(int(inputLayerUnits), int(hiddenLayerUnits),
int(outputLayerUnits))
print "generating input activations..."
net.genInputActivations()
print "loading weights..."
net.loadWeights("weights/" + action.replace(" ", "_"))
if raw_input("Continue training? (y/n): ") == "y":
try:
print "training..."
net.train()
except KeyboardInterrupt:
net.saveWeights()
print "weights saved"
except RuntimeError:
print "Training failed."
net.saveWeights()
print "weights saved"
#need to do minus/plus phase here
def trainVision(self):
net = NeuralNetwork("vision")
net.makeNetwork(1024, 25, 2)
if raw_input("Continue Previous Training? (y/n): ") == "y":
print "loading weights..."
net.loadWeights("weights/vision_2")
else:
print "initializing random weights..."
net.initializeRandomWeights()
print "generating input activations..."
net.genInputActivations()
try:
print "training..."
net.train()
except KeyboardInterrupt:
net.saveWeights()
print "weights saved"
def loadVision(self):
#index = input("Enter image index: ")
for index in range(len(self.vision.images["data"]) - 1):
self.vision.saveImage(index)
